ABSTRACT: This study aimed to compare spatial prediction results to map soil textural class names using laboratory results of soil textural properties (sand, silt, and clay) based on USDA soil textural triangle classes' boundary definitions. The study has been done in Dugda district, Oromiya Region, Ethiopia. The study area has been divided into 126 mapping units based on soil-forming factors and soil samples were collected at depths of 0 to 20 cm from each mapping unit. The soil samples were analyzed at the laboratory for textual proportions. These results were used in spatial prediction methods inverse distance weighted, ordinary kriging, and random forest to get continuous raster maps for sand, silt, and clay. The raster maps were used as input layers to develop the twelve soil textural.....
Keywords- IDW, OK, RF, Soil texture, spatial analysis, and USDA
[1]. Safari, Y., EsfandiarpourBoroujeni, I., Kamali, A., Salehi, M. H., & Bagheri Bodaghabadi, M. (2012). Mapping of the soil texture using geostatistical method (a case study of the Shahrekord plain, central Iran). Arabian Journal of Geosciences, 6(9), 3331–3339
[2]. Sugihara, S., Funakawa, S., Kilasara, M., &Kosaki, T. (2010). Effect of land management and soil texture on seasonal variations in soil microbial biomass in dry tropical agroecosystems in Tanzania. Applied Soil Ecology, 44(1), 80–88.
[3]. Huerta, E., & van der Wal, H. (2012). Soil macroinvertebrates' abundance and diversity in home gardens in Tabasco, Mexico, vary with soil texture, organic matter and vegetation cover. European Journal of Soil Biology, 50, 68–75.
[4]. Liao, K., Xu, S., Wu, J., & Zhu, Q. (2013). Spatial estimation of surface soil texture using remote sensing data. Soil Science and Plant Nutrition, 59(4), 488–500.
[5]. Gozdowski D, Stępień M, Samborski S, Dobers ES, Szatyłowicz J, Chormański J (2015) Prediction accuracy of selected spatial interpolation methods for soil texture at farm field scale. J Soil Sci Plant Nutr 15(3):639–650.